Accurately mapping the raw global position system (GPS) trajectories to the road network is the basis for studying the application of trajectory data. This study proposes a novel off-line map matching algorithm based on road network topology, to address the problems of low execution efficiency and poor matching accuracy of selective look-ahead map matching (SLAMM) algorithm. First, the noise points of the trajectory data are removed by data preprocessing. Second, the algorithm searches for critical samples in the trajectory data and segments the data accordingly. Then, the adjacent road segments around the transition node corresponding to the critical sample are selected as candidate arcs. Finally, the segmented trajectory data are matched to the road network by constructing an error ellipse. The algorithm fully considers the topology of the road network and the characteristics of high-frequency trajectory data. The experimental results, using Beijing trajectory data to perform matching on an actual road network environment, show that the proposed algorithm is more efficient and robust than other map matching algorithms for high-frequency trajectories.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    High-Frequency Trajectory Map Matching Algorithm Based on Road Network Topology


    Contributors:
    Yu, Qingying (author) / Hu, Fan (author) / Ye, Zhen (author) / Chen, Chuanming (author) / Sun, Liping (author) / Luo, Yonglong (author)

    Published in:

    Publication date :

    2022-10-01


    Size :

    2883859 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Urban Road Network Operation Quality Evaluation Method Based on High-Frequency Trajectory Data

    Lin, Pengfei / Weng, Jiancheng / Yin, Baocai et al. | IEEE | 2018



    A scenario-based map-matching algorithm for complex urban road network

    Kong, Xiangfu / Yang, Jiawen | Taylor & Francis Verlag | 2019


    A Path Increment Map Matching Method for High-Frequency Trajectory

    Haoyan, Wang / Yuangang, Liu / Shaohua, Li et al. | IEEE | 2023


    Vehicle trajectory prediction using road topology and traffic participant object states

    CHINNI KRISHNA MOHAN | European Patent Office | 2023

    Free access